In this paper, we will use the symbolic regression machine learning method proposed by Koza [1]}. Symbolic regression is a powerful tool for searching the mathematical space for an approximate functional relation between a certain number of input and output variables, and it is based on genetic programming proposed by
Holland [2]}. The framework of genetic programming is probabilistic, and is not based on mathematical principles, such as correctness, consistency, justifiability, certainty, orderliness, and decisiveness as outlined by Koza [1]}, but solely on the principles of Darwinian evolution [4]}. The idea of the genetic programming is simple, and it is based on transforming an initial population (in our case a population of mathematical functions) to a new population that survived a particular fitness constraint. The main operators that are used to create the new population are similar to those found in nature, namely that of reproduction and crossover
[1]} .
